vietrix/viena-60m-pretrain

Viena 60M (Pretrain)

Model details

• Developed by: Vietrix
• Model type: decoder-only causal LM (Llama-style)
• Parameters: ~60M
• Layers: 16
• Hidden size: 512
• Attention heads: 8 (KV heads: 4)
• Max sequence length: 1024
• RoPE theta: 10000
• Normalization/MLP: RMSNorm + SwiGLU
• Precision: BF16 training

Tokenizer

• SentencePiece BPE
• Target vocab in config: 32k
• Actual vocab in tokenizer.model: 2105 (trained on a small corpus)
• Note: embeddings are sized for 32k; only the first 2105 tokens are used by the tokenizer.

Training data

• Internal synthetic Vietnamese pretrain corpus.
• Domains: Vietnam/general, math, code, identity.
• Raw JSONL entries: ~2.4k; after cleanup/dedupe, HF dataset contains 472 unique texts.
• PII: best-effort redaction during dataset build.

Training procedure

• Objective: next-token prediction with packed sequences.
• Sequence length: 1024.
• Global batch size: 64 (batch 16 x grad_accum 4).
• Optimizer: AdamW, lr 3e-4, weight decay 0.1, cosine decay with 10% warmup.
• Steps: 2,500 (approx 163.8M tokens processed).
• Checkpoints saved every 1,250 steps.

Intended use

• Base model for continued training or fine-tuning on Vietnamese tasks.
• Not instruction-tuned; outputs may be unaligned.

Limitations

• Trained on a small synthetic corpus; coverage and factuality are limited.
• Not suitable for safety-critical or high-stakes applications.
• Tokenizer vocab is much smaller than model vocab; lexical coverage is limited.

How to use

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "vietrix/viena-60m-pretrain"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_id)

If AutoTokenizer fails, load the SentencePiece model explicitly:

from transformers import LlamaTokenizer

tokenizer = LlamaTokenizer.from_pretrained(model_id, use_fast=False)